概述
使用Python实现虚拟鼠标控制,利用手势识别来替代传统鼠标操作。这一实现依赖于计算机视觉库OpenCV、手势识别库MediaPipe以及其他辅助库如PyAutoGUI和Pynput。
环境配置
在开始之前,请确保已安装以下Python库:
pip install opencv-python mediapipe pynput pyautogui numpy pillow
模块介绍
1. utils.py
utils.py
包含一个Utils
类,主要提供在图像上添加中文文本的功能。这对于在实时视频流中显示信息非常有用。
代码解析
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
class Utils:
def __init__(self):
pass
def cv2AddChineseText(self, img, text, position, textColor=(0, 255, 0), textSize=30):
if isinstance(img, np.ndarray): # 判断是否OpenCV图片类型
img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(img)
fontStyle = ImageFont.truetype("./fonts/simsun.ttc", textSize, encoding="utf-8")
draw.text(position, text, textColor, font=fontStyle)
return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)
2. handProcess.py
handProcess.py
负责手势的识别和处理。该模块使用MediaPipe库来检测和跟踪手部的关键点,并根据手势的不同动作触发相应的鼠标操作。
代码解析
import cv2
import mediapipe as mp
import time
import math
import numpy as np
from utils import Utils
class HandProcess:
def __init__(self, static_image_mode=False, max_num_hands=2):
self.mp_drawing = mp.solutions.drawing_utils
self.mp_hands = mp.solutions.hands
self.hands = self.mp_hands.Hands(static_image_mode=static_image_mode,
min_detection_confidence=0.7,
min_tracking_confidence=0.5,
max_num_hands=max_num_hands)
self.landmark_list = []
self.action_labels = {
'none': '无',
'move': '鼠标移动',
'click_single_active': '触发单击',
'click_single_ready': '单击准备',
'click_right_active': '触发右击',
'click_right_ready': '右击准备',
'scroll_up': '向上滑页',
'scroll_down': '向下滑页',
'drag': '鼠标拖拽'
}
self.action_deteted = ''
def checkHandsIndex(self, handedness):
if len(handedness) == 1:
handedness_list = [handedness[0].classification[0].label]
else:
handedness_list = [handedness[0].classification[0].label, handedness[1].classification[0].label]
return handedness_list
def getDistance(self, pointA, pointB):
return math.hypot((pointA[0] - pointB[0]), (pointA[1] - pointB[1]))
def getFingerXY(self, index):
return (self.landmark_list[index][1], self.landmark_list[index][2])
def drawInfo(self, img, action):
thumbXY, indexXY, middleXY = map(self.getFingerXY, [4, 8, 12])
if action == 'move':
img = cv2.circle(img, indexXY, 20, (255, 0, 255), -1)
elif action == 'click_single_active':
middle_point = int((indexXY[0] + thumbXY[0]) / 2), int((indexXY[1] + thumbXY[1]) / 2)
img = cv2.circle(img, middle_point, 30, (0, 255, 0), -1)
elif action == 'click_single_ready':
img = cv2.circle(img, indexXY, 20, (255, 0, 255), -1)
img = cv2.circle(img, thumbXY, 20, (255, 0, 255), -1)
img = cv2.line(img, indexXY, thumbXY, (255, 0, 255), 2)
elif action == 'click_right_active':
middle_point = int((indexXY[0] + middleXY[0]) / 2), int((indexXY[1] + middleXY[1]) / 2)
img = cv2.circle(img, middle_point, 30, (0, 255, 0), -1)
elif action == 'click_right_ready':
img = cv2.circle(img, indexXY, 20, (255, 0, 255), -1)
img = cv2.circle(img, middleXY, 20, (255, 0, 255), -1)
img = cv2.line(img, indexXY, middleXY, (255, 0, 255), 2)
return img
def checkHandAction(self, img, drawKeyFinger=True):
upList = self.checkFingersUp()
action = 'none'
if len(upList) == 0:
return img, action, None
dete_dist = 100
key_point = self.getFingerXY(8)
if upList == [0, 1, 0, 0, 0]:
action = 'move'
if upList == [1, 1, 0, 0, 0]:
l1 = self.getDistance(self.getFingerXY(4), self.getFingerXY(8))
action = 'click_single_active' if l1 < dete_dist else 'click_single_ready'
if upList == [0, 1, 1, 0, 0]:
l1 = self.getDistance(self.getFingerXY(8), self.getFingerXY(12))
action = 'click_right_active' if l1 < dete_dist else 'click_right_ready'
if upList == [1, 1, 1, 1, 1]:
action = 'scroll_up'
if upList == [0, 1, 1, 1, 1]:
action = 'scroll_down'
if upList == [0, 0, 1, 1, 1]:
key_point = self.getFingerXY(12)
action = 'drag'
img = self.drawInfo(img, action) if drawKeyFinger else img
self.action_deteted = self.action_labels[action]
return img, action, key_point
def checkFingersUp(self):
fingerTipIndexs = [4, 8, 12, 16, 20]
upList = []
if len(self.landmark_list) == 0:
return upList
if self.landmark_list[fingerTipIndexs[0]][1] < self.landmark_list[fingerTipIndexs[0] - 1][1]:
upList.append(1)
else:
upList.append(0)
for i in range(1, 5):
if self.landmark_list[fingerTipIndexs[i]][2] < self.landmark_list[fingerTipIndexs[i] - 2][2]:
upList.append(1)
else:
upList.append(0)
return upList
def processOneHand(self, img, drawBox=True, drawLandmarks=True):
utils = Utils()
results = self.hands.process(img)
self.landmark_list = []
if results.multi_hand_landmarks:
for hand_index, hand_landmarks in enumerate(results.multi_hand_landmarks):
if drawLandmarks:
self.mp_drawing.draw_landmarks(img, hand_landmarks, self.mp_hands.HAND_CONNECTIONS,
self.mp_drawing_styles.get_default_hand_landmarks_style(),
self.mp_drawing_styles.get_default_hand_connections_style())
for landmark_id, finger_axis in enumerate(hand_landmarks.landmark):
h, w, c = img.shape
p_x, p_y = math.ceil(finger_axis.x * w), math.ceil(finger_axis.y * h)
self.landmark_list.append([landmark_id, p_x, p_y, finger_axis.z])
if drawBox:
x_min, x_max = min(self.landmark_list, key=lambda i: i[1])[1], max(self.landmark_list, key=lambda i: i[1])[1]
y_min, y_max = min(self.landmark_list, key=lambda i: i[2])[2], max(self.landmark_list, key=lambda i: i[2])[2]
img = cv2.rectangle(img, (x_min - 30, y_min - 30), (x_max + 30, y_max + 30), (0, 255,
0), 2)
img = utils.cv2AddChineseText(img, self.action_deteted, (x_min - 20, y_min - 120), textColor=(255, 0, 255), textSize=60)
return img
3. virtual_mouse.py
virtual_mouse.py
是主程序模块,整合了手势识别和鼠标控制功能,实现了通过手势控制鼠标移动、点击和滚动的功能。
代码解析
import cv2
import handProcess
import time
import numpy as np
import pyautogui
from utils import Utils
from pynput.mouse import Button, Controller
class VirtualMouse:
def __init__(self):
self.image = None
self.mouse = Controller()
def recognize(self):
handprocess = handProcess.HandProcess(False, 1)
utils = Utils()
fpsTime = time.time()
cap = cv2.VideoCapture(0)
resize_w = 960
resize_h = 720
frameMargin = 100
screenWidth, screenHeight = pyautogui.size()
stepX, stepY = 0, 0
finalX, finalY = 0, 0
smoothening = 7
action_trigger_time = {
'single_click': 0,
'double_click': 0,
'right_click': 0
}
mouseDown = False
while cap.isOpened():
action_zh = ''
success, self.image = cap.read()
self.image = cv2.resize(self.image, (resize_w, resize_h))
if not success:
print("空帧")
continue
self.image.flags.writeable = False
self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
self.image = cv2.flip(self.image, 1)
self.image = handprocess.processOneHand(self.image)
cv2.rectangle(self.image, (frameMargin, frameMargin), (resize_w - frameMargin, resize_h - frameMargin), (255, 0, 255), 2)
self.image, action, key_point = handprocess.checkHandAction(self.image, drawKeyFinger=True)
action_zh = handprocess.action_labels[action]
if key_point:
x3 = np.interp(key_point[0], (frameMargin, resize_w - frameMargin), (0, screenWidth))
y3 = np.interp(key_point[1], (frameMargin, resize_h - frameMargin), (0, screenHeight))
finalX = stepX + (x3 - stepX) / smoothening
finalY = stepY + (y3 - stepY) / smoothening
now = time.time()
if action_zh == '鼠标拖拽':
if not mouseDown:
self.mouse.press(Button.left)
mouseDown = True
self.mouse.position = (finalX, finalY)
else:
if mouseDown:
self.mouse.release(Button.left)
mouseDown = False
if action_zh == '鼠标移动':
self.mouse.position = (finalX, finalY)
elif action_zh == '单击准备':
pass
elif action_zh == '触发单击' and (now - action_trigger_time['single_click'] > 0.3):
self.mouse.click(Button.left, 1)
action_trigger_time['single_click'] = now
elif action_zh == '右击准备':
pass
elif action_zh == '触发右击' and (now - action_trigger_time['right_click'] > 2):
self.mouse.click(Button.right, 1)
action_trigger_time['right_click'] = now
elif action_zh == '向上滑页':
pyautogui.scroll(30)
elif action_zh == '向下滑页':
pyautogui.scroll(-30)
stepX, stepY = finalX, finalY
self.image.flags.writeable = True
self.image = cv2.cvtColor(self.image, cv2.COLOR_RGB2BGR)
cTime = time.time()
fps_text = 1 / (cTime - fpsTime)
fpsTime = cTime
self.image = utils.cv2AddChineseText(self.image, "帧率: " + str(int(fps_text)), (10, 30), textColor=(255, 0, 255), textSize=50)
self.image = cv2.resize(self.image, (resize_w // 2, resize_h // 2))
cv2.imshow('virtual mouse', self.image)
if cv2.waitKey(5) & 0xFF == 27:
break
cap.release()
control = VirtualMouse()
control.recognize()
4. 功能列表
在这个虚拟鼠标控制项目中,通过识别不同的手势来触发相应的鼠标操作。以下是该项目中实现的主要功能及其对应的手势:
鼠标移动
- 手势:食指竖起(其他手指收回)。
- 描述:食指指尖的移动映射到屏幕上的鼠标光标移动。
单击准备
- 手势:拇指和食指都竖起且未接触。
- 描述:准备触发单击。
触发单击
- 手势:拇指和食指接触(捏合)。
- 描述:触发一次鼠标左键单击。
右击准备
- 手势:食指和中指都竖起且未接触。
- 描述:准备触发右击。
触发右击
- 手势:食指和中指接触(捏合)。
- 描述:触发一次鼠标右键单击。
鼠标拖拽
- 手势:中指、无名指和小指竖起(拇指和食指收回)。
- 描述:模拟鼠标左键按住并拖动。
向上滚动
- 手势:五指全部竖起。
- 描述:触发页面向上滚动。
向下滚动
- 手势:除了拇指外,其他四指竖起。
- 描述:触发页面向下滚动。